Falls are a public health concern and cause for institutionalization in the senescent population, for whom they disproportionately affect. Loosely defined as an unintentional and uncontrolled movement towards the ground or lower level, a fall can have debilitating and sometimes fatal consequences. Although falls increase rates of morbidity and mortality, earlier detection and reporting of such events can improve outcomes.
Practical, early detection and reporting of falls has been an elusive goal. Efforts to detect falls have classically employed wearable technologies to capture user input (e.g., panic button press) or to characterize and classify movements and postures. Although these technologies demonstrate reasonable utility in ideal conditions, user non-compliance and fall-related incapacitation reduce general efficacy in application. Furthermore, inability to verify incidence of detected falls (e.g., both true and false) leads to inaccurate fall reporting and undesirable handling of potential fall events.